OPSCC_TCGA
Avishai Wizel
2025-09-03 11:29:00.690383
if (interactive()) {
source("./pipe.R")
input = get_input(get_current_path())
output = get_output(get_current_path())
}paged_table(data.frame(value= unlist(params)))1 Data
library(TCGAbiolinks)## Warning: multiple methods tables found for 'sort'
## Warning: multiple methods tables found for 'sort'library(biomaRt)
library(kableExtra)
library(rmarkdown)
library(readxl)
source(input$functions)
source(input$fc_params)
suppressWarnings(library(GSEABase))
library(grid)
library(ComplexHeatmap)
library(ggpubr)
library(stringr)
library(data.table)
library(ggstatsplot)# load hpv signature genes
avg_log2FC_cutoff = log2(signature_fc)
fdr_cutoff = signature_fdr
opscc_deg = read.table(file = input$opscc_deg,row.names = 1,sep = ",",header = T)
hmsc_deg = read.table(file = input$hmsc_deg,row.names = 1,sep = ",",header = T)
hmsc_signatue = rownames(hmsc_deg[hmsc_deg$fdr<fdr_cutoff & hmsc_deg$avg_log2FC>avg_log2FC_cutoff,])
opscc_signature = opscc_deg %>% filter(avg_log2FC > avg_log2FC_cutoff &
fdr < fdr_cutoff) %>% rownames()
message("opscc_signature:")## opscc_signature:print(opscc_signature)## [1] "HPV16-E1" "HPV16-E7" "HPV16-E5" "FBL" "DST" "ALDH3A1" "IGFBP2" "KRT15" "SERTAD1"message("hmsc_signatue:")## hmsc_signatue:print(hmsc_signatue)## [1] "ANLN" "TUBB6" "AXL" "IFT122" "GFM1" "SEPT2" "PBRM1" "INSIG1"
## [9] "CAPG" "POP7" "BHLHE40" "MCM5" "TRAPPC3" "MRPS6" "GINS2" "NARS"
## [17] "LSM5" "GUSB" "EZR" "FZD6" "STAT2" "TMPO" "SLC35F5" "MVB12A"
## [25] "CDC42EP3" "COG4" "MRPL20" "GSDMD" "VCL" "MT1E" "TUBA1C" "TXN"
## [33] "ATXN10" "USP39" "MSH6" "SDC2" "OAT" "SCAP" "PRELID3B" "KNTC1"
## [41] "ARF6" "SMC5" "DCTN2" "AAMP" "TBCB" "KLF10" "TPP1" "IFT52"
## [49] "ETS2" "EWSR1" "NOP10" "KRCC1" "ASL" "IGSF9" "PHF5A" "XRCC5"
## [57] "UBE2T" "IK" "AC007250.3" "YES1" "SNRPD1" "C9orf3" "FDFT1" "TPM3"
## [65] "LASP1" "SNRPC" "PACSIN2" "MTHFD2" "PMVK" "OBP2B" "AKR1B1" "SYPL1"
## [73] "LPAR2" "PHB" "THOC6" "TMX1" "SDHA" "NUBP2" "NDUFS7" "TAOK3"
## [81] "MRPS9" "PTRF" "PSME2" "MED15" "PES1" "RP11-19E11.1" "RP5-1102E8.2" "SEH1L"
## [89] "MCM3" "POLR2L" "DNAJC8" "COPS6" "PRKCDBP" "KRT16" "RNASEH2A" "PDIA6"
## [97] "RAB11FIP1" "RFC3" "POLD2" "AC079337.1" "NF2" "BABAM1" "TPGS2" "LOX"
## [105] "SERINC3" "NDUFB8" "CLIC4" "SELT" "GRB2" "HELLS" "CXCR4" "PRDX5"
## [113] "FLII" "EDIL3" "SF3A3" "PSMB1" "FARSA" "PREB" "POMP" "MPPED2"
## [121] "CKB" "MCOLN1" "MCM4" "PCNT" "BTF3L4" "DDX1" "BRE" "GPN1"
## [129] "H2AFZ" "KIF22" "PSMB4" "TMEM165" "RP11-45P22.1" "ECH1" "NUCB2" "NUDCD3"
## [137] "C19orf10" "VPS4B" "MGME1" "TCF19" "KRT5" "ATP6V0B" "GTF2I" "TSC2"
## [145] "CNOT7" "UFD1L" "SMARCA5" "TCERG1" "PLAUR" "SSRP1" "SMC3" "RASSF1"
## [153] "TYMS" "GPC4" "CNPPD1" "PLXDC2" "UBXN11" "RAD23A" "USP4" "C16orf13"
## [161] "CTNNAL1" "LRRCC1" "ADNP" "SDHB" "UQCRQ" "MTHFD1" "MRPL21" "RANGAP1"
## [169] "PSMA5" "BRIX1" "RP11-665C16.9" "FDPS" "FHL2" "MYC" "KLHDC3" "DNAJB6"
## [177] "RP5-1085F17.3" "DCTPP1" "RBM8A" "UBE2L3" "MRE11A" "MTX2" "HADHA" "NRP2"
## [185] "LYPLA2" "SHKBP1" "GLOD4" "ATP6V1H" "EIF4E2" "NME1" "HDAC2" "JUP"
## [193] "PHLDB1" "NUTF2" "CCT7" "STRAP" "TES" "BUB3" "DLX5" "SYF2"
## [201] "GPBP1L1" "DDB1" "EZH2" "SNRPA1" "PDCD6" "EMC2" "RP4-702J19.1" "CAV1"
## [209] "CDK4" "CCT5" "RAB1A" "CDKN1A" "ELAVL1" "MBD4" "PLEKHJ1" "CCT3"
## [217] "ATPIF1" "CDCA7" "C22orf29" "SEC23B" "SPIDR" "HSPB8" "UBE2I" "CPNE3"
## [225] "NEFH" "LMAN1" "RAB18" "ILK" "EIF2A" "SLBP" "ELOVL1" "MYB"
## [233] "RP4-753D5.4" "KRT18" "CRTAP" "MUM1" "TPI1" "KIAA1715" "RP11-259K5.3" "DTYMK"
## [241] "MAEA" "PNRC2" "GLT25D1" "MCM6" "RBM17" "TTC39A" "FEN1" "SHARPIN"
## [249] "IFITM1" "PSMD4" "SNORD99" "BZW2" "TIMMDC1" "STAG3" "TPD52" "SNRPG"
## [257] "TIMM13" "JAG1" "BTG2" "LAMTOR1" "AC019188.1" "VAMP3" "CAD" "ATAD2"myb_targets <- read_excel(input$myb_targets,col_names = T) %>% pull(Gene)
tp53_data = read_tsv(file = input$tp53_data,col_names = T)## Rows: 530 Columns: 8
## [36mℹ[39m Targets are out of date. Updating...## [36mℹ[39m Targets are out of date. Updating...── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## [36mℹ[39m Targets are out of date. Updating...Delimiter: "\t"
## chr (7): Study ID, Sample ID, Patient ID, TP53, TP53: MUT, TP53: AMP, TP53: HOMDEL
## dbl (1): Altered
##
## [36mℹ[39m Targets are out of date. Updating...
##
## [36mℹ[39m Targets are out of date. Updating...ℹ Use `spec()` to retrieve the full column specification for this data.
## [36mℹ[39m Targets are out of date. Updating...ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## [36mℹ[39m Targets are out of date. Updating...# load TPM data
HNSC_tpm = readRDS(input$HNSC_tpm)
if (params$log_tpm) {
HNSC_tpm = log2(HNSC_tpm+1)
}# load clinical data
project_name = "TCGA-HNSC"
clinical_data <- GDCquery_clinic(project_name, "clinical")
OPSCC_tissues = c("Base of tongue, NOS", #filter only OPSCC
"Oropharynx, NOS",
"Posterior wall of oropharynx",
"Tonsil, NOS")
clinical_data = clinical_data[clinical_data$tissue_or_organ_of_origin %in% OPSCC_tissues,]#add follow_up data
clinical_follow_up = read_tsv(input$clinical_follow_up) %>% as.data.frame()## New names:
## Rows: 1949 Columns: 139 [36mℹ[39m Targets are out of date. Updating...
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## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── [36mℹ[39m Targets are out of
## date. Updating...Delimiter: "\t" chr (139): case_id, case_submitter_id, project_id, adverse_event, adverse_event_grade, aids_risk_factors,
## barretts_esophagus_goblet_cells_prese...
##
## [36mℹ[39m Targets are out of date. Updating...
## [36mℹ[39m Targets are out of date. Updating...ℹ Use `spec()` to retrieve the full column specification for this data. [36mℹ[39m Targets are out of date.
## Updating...ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. [36mℹ[39m Targets are out of date. Updating...
## • `timepoint_category` -> `timepoint_category...79`
## • `timepoint_category` -> `timepoint_category...135`#get only "Last Contact"
clinical_follow_up <- clinical_follow_up[clinical_follow_up$timepoint_category...135 == "Last Contact", ]
#add to clinical and rename days_to_last_follow_up> days_to_follow_up
clinical_data = inner_join(clinical_data,clinical_follow_up[,c("case_submitter_id","days_to_follow_up"),drop=F],by = c("submitter_id"="case_submitter_id")) %>% select(-days_to_last_follow_up) %>% dplyr::rename(days_to_last_follow_up = days_to_follow_up)# add HPV statusfrom cbioportal to clinical_data
cbp_data = read_tsv(file = input$cbp_data)## Rows: 523 Columns: 62
## [36mℹ[39m Targets are out of date. Updating...
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## [36mℹ[39m Targets are out of date. Updating...Delimiter: "\t"
## chr (43): Study ID, Patient ID, Sample ID, Neoplasm Disease Stage American Joint Committee on Cancer Code, American Joint Committee on Cancer P...
## dbl (18): Diagnosis Age, Aneuploidy Score, Buffa Hypoxia Score, Last Communication Contact from Initial Pathologic Diagnosis Date, Birth from I...
## lgl (1): Patient Weight
##
## [36mℹ[39m Targets are out of date. Updating...
##
## [36mℹ[39m Targets are out of date. Updating...ℹ Use `spec()` to retrieve the full column specification for this data.
## [36mℹ[39m Targets are out of date. Updating...ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## [36mℹ[39m Targets are out of date. Updating...cbp_data = cbp_data[,c("Patient ID", "Subtype")] %>% dplyr::rename(hpv_status = Subtype) %>%
mutate(hpv_status = gsub(,x = hpv_status,pattern = "HNSC_HPV",replacement = "HPV"))
clinical_data = inner_join(x = clinical_data,y = cbp_data,by = c("submitter_id"="Patient ID"))
na_patients = clinical_data[is.na(clinical_data$hpv_status), "submitter_id"]# add TP53 mutation status from cbioportal to clinical_data
tp53_data = tp53_data[,c("Patient ID", "Altered")] %>% dplyr::rename(tp53_status = Altered) %>%
mutate(tp53_status =
case_when(tp53_status == 1 ~ "tp53_mutated",
tp53_status == 0 ~ "tp53_unmutated"))
clinical_data = inner_join(x = clinical_data,y = tp53_data,by = c("submitter_id"="Patient ID"))
na_patients = clinical_data[is.na(clinical_data$tp53_data), "submitter_id"]# #set clinical data to max 5 years
# clinical_data = clinical_data %>% mutate(days_to_last_follow_up = as.numeric(days_to_last_follow_up)) %>%
# mutate(vital_status = if_else(condition = (days_to_death) > 1825 | is.na(days_to_death) , false = vital_status, true = "Alive")) %>%
# mutate(days_to_last_follow_up = if_else(condition = days_to_last_follow_up > 1825, false = days_to_last_follow_up,true = 1825)) %>%
# mutate(days_to_death = if_else(condition = days_to_death > 1825, false = days_to_death, true = NA))2 MYB
gene_status = set_clinical_data(clin_data = clinical_data,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
paged_table(gene_status)clinical_data_with_scores = clinical_data[clinical_data$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set\n GBM",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
pdf(paste0(params$data_out_dir,"KM_MYB_OPSCC.pdf"),width = 8,height = 6)
p[[1]]/p[[2]]
dev.off()## png
## 23 HPV
clinical_data_for_hpv = clinical_data %>% filter(!is.na(hpv_status))
p = TCGAanalyze_survival(
data = clinical_data_for_hpv,
clusterCol = "hpv_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
4 MYB - HPV PPM correlation
# load data
library("readxl")
library(stringr)
library(ggpubr)
TCGA_HPV_data <- read_excel("./Input_data/TCGA/PMC3806554_ncomms3513-s5.xlsx",skip = 2)
TCGA_HPV_data$submitter_id = TCGA_HPV_data$`Sample Barcode` %>% str_sub( start = 1, end = 12)
rownames(TCGA_HPV_data) = TCGA_HPV_data$submitter_id## Warning: Setting row names on a tibble is deprecated.TCGA_HPV_data = TCGA_HPV_data %>% filter(Cancer == "HNSC")gene_status = set_clinical_data(clin_data = clinical_data,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status = gene_status[TCGA_HPV_data$submitter_id,] %>% cbind(HPV_PPM = TCGA_HPV_data$ppm) %>% na.omit()
paged_table(gene_status)sp <- ggscatter(gene_status, x = "MYB", y = "HPV_PPM",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
)
# Add correlation coefficient
sp + stat_cor(method = "pearson")## `geom_smooth()` using formula = 'y ~ x'
5 HPV signature - HPV correlation
# with HPV signature
gene_status = set_clinical_data(clin_data = clinical_data,genes = opscc_signature,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HPV+ signature")
gene_status = gene_status[TCGA_HPV_data$submitter_id,] %>% cbind(HPV_PPM = TCGA_HPV_data$ppm) %>% na.omit()
paged_table(gene_status)sp <- ggscatter(gene_status, x = "HPV+ signature", y = "HPV_PPM",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
)
# Add correlation coefficient
sp + stat_cor(method = "pearson")## `geom_smooth()` using formula = 'y ~ x'
6 HPV signature - HPV T test
# create data
gene_status = set_clinical_data(clin_data = clinical_data,genes = opscc_signature,tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","hpv_status")],by = c("patient"="submitter_id"))
paged_table(gene_status)# plot
library(ggpubr)
library(rstatix)
stat.test <- gene_status %>%
t_test(score ~ hpv_status) %>%
add_significance()
stat.testbxp <- ggboxplot(gene_status, x = "hpv_status", y = "score", fill = "#00AFBB")+
geom_jitter()
stat.test <- stat.test %>% add_xy_position(x = "hpv_status")
bxp +
stat_pvalue_manual(stat.test, label = "T-test, p = {p}") 
7 MYB- HPV T test
# create data
gene_status = set_clinical_data(clin_data = clinical_data,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","hpv_status")],by = c("patient"="submitter_id"))
paged_table(gene_status)# plot
library(ggpubr)
library(rstatix)
gene_status$hpv_status = factor(gene_status$hpv_status,levels = c("HPV-","HPV+"))
stat.test <- gene_status %>%
t_test(MYB ~ hpv_status) %>%
add_significance()
stat.teststat.test <- stat.test %>% add_xy_position(x = "hpv_status")
plt = ggplot(gene_status, aes(x = hpv_status, y = MYB)) +
geom_violin(scale = "width",aes(fill = hpv_status)) +
geom_boxplot(width = 0.2, outlier.shape = NA) +
stat_pvalue_manual(stat.test, label = "{p.adj}") + #add p value
geom_jitter()+
labs(title = "MYB expression by HPV status", y = "MYB expression: log2(TPM)", fill = "HPV Status")+
theme(plot.title = element_text(hjust = 0.5),axis.title.x = element_blank())+scale_x_discrete(limits = c("HPV-","HPV+"))+theme_minimal()
plt## Warning: Removed 5 rows containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Removed 5 rows containing missing values or values outside the scale range (`stat_boxplot()`).## Warning: Removed 5 rows containing missing values or values outside the scale range (`geom_point()`).
pdf(paste0(params$data_out_dir,"violin_MYB_HPV_OPSCC_TCGA.pdf"),width = 7,height = 5)
plt## Warning: Removed 5 rows containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Removed 5 rows containing missing values or values outside the scale range (`stat_boxplot()`).## Warning: Removed 5 rows containing missing values or values outside the scale range (`geom_point()`).dev.off()## png
## 28 MYB and HPV survival plot
8.1 HPV + tumors
clinical_data_with_scores = clinical_data %>% filter(hpv_status == "HPV+")
gene_status = set_clinical_data(clin_data = clinical_data_with_scores,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = "T")
paged_table(gene_status)gene_status$patient = rownames(gene_status)
clinical_data_with_scores = inner_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
8.2 HPV- tumors
clinical_data_with_scores = clinical_data %>% filter(hpv_status == "HPV-")
gene_status = set_clinical_data(clin_data = clinical_data_with_scores,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
paged_table(gene_status)gene_status$patient = rownames(gene_status)
clinical_data_with_scores = inner_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
8.3 combine MYB and HPV survival plot
gene_status = set_clinical_data(clin_data = clinical_data,genes = "MYB",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = inner_join(x = clinical_data,y = gene_status, by = c("submitter_id"="patient"))
clinical_data_with_scores$gene_status = paste(clinical_data_with_scores$gene_status,clinical_data_with_scores$hpv_status,sep = ", ")
paged_table(clinical_data_with_scores)p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set\n GBM",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
9 Myb Targets
# create data
gene_status = set_clinical_data(clin_data = clinical_data,genes = "NOTCH1",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","hpv_status")],by = c("patient"="submitter_id"))
paged_table(gene_status)# plot
library(ggpubr)
library(rstatix)
stat.test <- gene_status %>%
t_test(NOTCH1 ~ hpv_status) %>%
add_significance()
stat.testbxp <- ggboxplot(gene_status, x = "hpv_status", y = "NOTCH1", fill = "#00AFBB")+
geom_jitter()
stat.test <- stat.test %>% add_xy_position(x = "hpv_status")
bxp +
stat_pvalue_manual(stat.test, label = "T-test, p = {p}") +ylim(0,7)
10 HMSC signature
# all samples:
gene_status = set_clinical_data(clin_data = clinical_data,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HPV+ signature")
clinical_data_with_scores = clinical_data[clinical_data$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set\n GBM",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC samples"))/p[[2]]
print_tab(p1,title = "all samples")all samples
pdf(paste0(params$data_out_dir,"all_samples_hmsc_signature.pdf"),width = 8,height = 6)
p1
dev.off()png 2
# HPV+ samples:
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV+")
gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC HPV+ samples"))/p[[2]]
print_tab(p1,title = "HPV+")HPV+
pdf(paste0(params$data_out_dir,"HPVpos_samples_hmsc_signature.pdf"),width = 8,height = 6)
p1
dev.off()png 2
# HPV neg samples:
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV-")
gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature")
clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC HPV- samples"))/p[[2]]
print_tab(p1,title = "HPV-")HPV-
pdf(paste0(params$data_out_dir,"HPVneg_samples_hmsc_signature.pdf"),width = 8,height = 6)
p1
dev.off()png 2
10.1 No cell cycle genes
# remove genes:
genesets_h = geneIds(getGmt(input$genesets_h))
message("cc genes in signature:")## cc genes in signature:print(hmsc_signatue[hmsc_signatue %in% genesets_h$GO_MITOTIC_CELL_CYCLE])[1] “ANLN” “TUBB6” “PBRM1” “MCM5” “TUBA1C” “MSH6” “KNTC1” “SMC5”
“DCTN2” “IK” “TAOK3” “PSME2” “SEH1L” “MCM3” “PSMB1” [16] “MCM4” “PCNT”
“KIF22” “PSMB4” “VPS4B” “CNOT7” “SMC3” “TYMS” “PSMA5” “MYC” “BUB3”
“SYF2” “DDB1” “EZH2” “CDK4”
[31] “CDKN1A” “MYB” “MAEA” “MCM6” “PSMD4” “BTG2”
hmsc_signatue_no_cc = hmsc_signatue[!hmsc_signatue %in% genesets_h$GO_MITOTIC_CELL_CYCLE]
#all samples:
gene_status = set_clinical_data(clin_data = clinical_data,genes = hmsc_signatue_no_cc,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature no CC")
clinical_data_with_scores = clinical_data[clinical_data$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC samples"))/p[[2]]
print_tab(p1,title = "OPSCC samples",subtitle_num = 3)OPSCC samples
pdf(paste0(params$data_out_dir,"all_samples_hmsc_signature_noCC_genes.pdf"),width = 8,height = 6)
p1
dev.off()png 2
#hpv +:
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV+")
gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = hmsc_signatue_no_cc,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature no CC")
clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC HPV+ samples"))/p[[2]]
print_tab(p1,title = "HPV+",subtitle_num = 3)HPV+
pdf(paste0(params$data_out_dir,"HPVpos_samples_hmsc_signature_noCC_genes.pdf"),width = 8,height = 6)
p1
dev.off()png 2
#hpv- :
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV-")
gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = hmsc_signatue_no_cc,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature no CC")
clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p1 = (p[[1]]+ggtitle("OPSCC HPV- samples"))/p[[2]]
print_tab(p1,title = "HPV-",subtitle_num = 3)HPV-
pdf(paste0(params$data_out_dir,"HPVneg_samples_hmsc_signature_noCC_genes.pdf"),width = 8,height = 6)
p1
dev.off()png 2
10.2 T test
# create data
gene_status = set_clinical_data(clin_data = clinical_data,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","hpv_status")],by = c("patient"="submitter_id"))
gene_status = gene_status %>% filter(!is.na(hpv_status))
paged_table(gene_status)# plot
library(ggpubr)
library(rstatix)
stat.test <- gene_status %>%
t_test(score ~ hpv_status) %>%
add_significance()
stat.testbxp <- ggboxplot(gene_status, x = "hpv_status", y = "score", fill = "#00AFBB")+
geom_jitter()
stat.test <- stat.test %>% add_xy_position(x = "hpv_status")
bxp +
stat_pvalue_manual(stat.test, label = "T-test, p = {p}") 
# HPV signature ~ stage
gene_status = set_clinical_data(clin_data = clinical_data,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","ajcc_clinical_stage","hpv_status")],by = c("patient"="submitter_id"))
gene_status = gene_status %>% filter(!is.na(ajcc_clinical_stage))
paged_table(gene_status)gene_status$ajcc_clinical_stage = factor(gene_status$ajcc_clinical_stage,levels = c("Stage I", "Stage II", "Stage III","Stage IVA", "Stage IVB",
"Stage IVC"))my_comparisons <- list( c("Stage I", "Stage II"),c("Stage II", "Stage III"),c("Stage III", "Stage IVA"),c("Stage IVA", "Stage IVB"),c("Stage IVB", "Stage IVC") )
p_list = list()
for (subset_name in list("HPV+","HPV-",c("HPV+","HPV-"))) {
gene_status_subset = gene_status %>% filter(hpv_status %in% subset_name)
stat.test <-
compare_means(score ~ ajcc_clinical_stage,data = gene_status_subset,
method = "wilcox.test") %>%
filter(paste(group1, group2) %in% unlist(lapply(my_comparisons, paste, collapse = " "))) %>%
mutate(p = round(p, 3))
stat.test = stat.test %>% add_y_position(formula = score ~ ajcc_clinical_stage,data = gene_status_subset,comparisons = my_comparisons,step.increase = 0)
p <- ggboxplot(gene_status_subset, x = "ajcc_clinical_stage", y = "score",
color = "ajcc_clinical_stage", palette = "npg",
add = "jitter",)+
stat_pvalue_manual(stat.test, label = "p = {p}",bracket.shorten = 0.1)+
ylab("HMSC_HPV_score")+
ggtitle("OPSCC TCGA " %s+% paste0(subset_name,collapse = " and ") %s+% " samples")+
theme(legend.position = "none")
p_list[[paste0(subset_name,collapse = " and ")]] = p
print(p)
}
pdf(file = params$data_out_dir %s+% "HPV_signature_score-stage_OPSCC_TCGA.pdf")
lapply(p_list,function(x) {x})## $`HPV+`##
## $`HPV-`##
## $`HPV+ and HPV-`dev.off()## png
## 210.3 HPV signature ~ stage no CC genes
gene_status = set_clinical_data(clin_data = clinical_data,genes = hmsc_signatue_no_cc,tpm_data_frame = HNSC_tpm,stratify = params$stratify)
gene_status$patient = rownames(gene_status)
gene_status = inner_join(x = gene_status,y = clinical_data[,c("submitter_id","ajcc_clinical_stage","hpv_status")],by = c("patient"="submitter_id"))
gene_status = gene_status %>% filter(!is.na(ajcc_clinical_stage))
paged_table(gene_status)gene_status$ajcc_clinical_stage = factor(gene_status$ajcc_clinical_stage,levels = c("Stage I", "Stage II", "Stage III","Stage IVA", "Stage IVB",
"Stage IVC"))my_comparisons <- list( c("Stage I", "Stage II"),c("Stage II", "Stage III"),c("Stage III", "Stage IVA"),c("Stage IVA", "Stage IVB"),c("Stage IVB", "Stage IVC") )
p_list = list()
for (subset_name in list("HPV+","HPV-",c("HPV+","HPV-"))) {
gene_status_subset = gene_status %>% filter(hpv_status %in% subset_name)
stat.test <-
compare_means(score ~ ajcc_clinical_stage,data = gene_status_subset,
method = "wilcox.test") %>%
filter(paste(group1, group2) %in% unlist(lapply(my_comparisons, paste, collapse = " "))) %>%
mutate(p = round(p, 3))
stat.test = stat.test %>% add_y_position(formula = score ~ ajcc_clinical_stage,data = gene_status_subset,comparisons = my_comparisons,step.increase = 0)
p <- ggboxplot(gene_status_subset, x = "ajcc_clinical_stage", y = "score",
color = "ajcc_clinical_stage", palette = "npg",
add = "jitter",)+
stat_pvalue_manual(stat.test, label = "p = {p}",bracket.shorten = 0.1)+
ylab("HMSC_HPV_score_no_CC")+
ggtitle("OPSCC TCGA " %s+% paste0(subset_name,collapse = " and ") %s+% " samples")+
theme(legend.position = "none")
p_list[[paste0(subset_name,collapse = " and ")]] = p
print(p)
}
pdf(file = params$data_out_dir %s+% "HPV_signature_score-stage_OPSCC_TCGA_no_cc.pdf")
lapply(p_list,function(x) {x})## $`HPV+`##
## $`HPV-`##
## $`HPV+ and HPV-`dev.off()## png
## 211 E2F ~ MYB
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV+")
clinical_data_hpv_neg = clinical_data %>% filter(hpv_status == "HPV-")
all_figs = list()
for (gene in c("E2F1","E2F2","E2F3")) {
opscc_tpm = HNSC_tpm[,colnames(HNSC_tpm) %in% clinical_data_hpv_pos$submitter_id] #filter for OPSCC tissues as in clinical data
opscc_tpm = opscc_tpm[c("MYB",gene),] %>% t()
sp1 <- ggscatter(opscc_tpm, x = "MYB", y = gene,
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
) +
stat_cor(method = "pearson")+ # Add correlation coefficient
ggtitle("OPSCC TCGA HPV+ samples")+
ylab(gene %s+% " log(TPM)")+
xlab("MYB log(TPM)")
sp1
# with seperate HPV- cell analysis
# opscc_tpm = HNSC_tpm[,colnames(HNSC_tpm) %in% clinical_data_hpv_neg$submitter_id] #filter for OPSCC tissues as in clinical data
# opscc_tpm = opscc_tpm[c("MYB",gene),] %>% t()
#
# sp2 <- ggscatter(opscc_tpm, x = "MYB", y = gene,
# add = "reg.line", # Add regressin line
# add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
# conf.int = TRUE # Add confidence interval
# ) +
# stat_cor(method = "pearson")+ # Add correlation coefficient
# ggtitle("OPSCC TCGA HPV- samples")+
# ylab(gene %s+% " log(TPM)")+
# xlab("MYB log(TPM)")
# sp2
# all_figs[[gene]] = sp1/sp2
all_figs[[gene]] = sp1
}pdf(file = params$data_out_dir %s+% "TCGA MYB-ETF correlation_HPV_pos.pdf",height = 5,width = 6)
lapply(all_figs,function(x) {x})## $E2F1## `geom_smooth()` using formula = 'y ~ x'##
## $E2F2## `geom_smooth()` using formula = 'y ~ x'##
## $E2F3## `geom_smooth()` using formula = 'y ~ x'dev.off()## png
## 212 MYB ~ RBL1
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV+")
opscc_tpm = HNSC_tpm[,colnames(HNSC_tpm) %in% clinical_data_hpv_pos$submitter_id] #filter for OPSCC tissues as in clinical data
opscc_tpm = opscc_tpm[c("MYB","RBL1"),] %>% t()
sp <- ggscatter(opscc_tpm, x = "MYB", y = "RBL1",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
) +
stat_cor(method = "pearson")+ # Add correlation coefficient
ggtitle("OPSCC TCGA samples")+
ylab("E2F1 log(TPM)")+
xlab("MYB log(TPM)")
sp## `geom_smooth()` using formula = 'y ~ x'
clinical_data_hpv_neg = clinical_data %>% filter(hpv_status == "HPV-")
opscc_tpm = HNSC_tpm[,colnames(HNSC_tpm) %in% clinical_data_hpv_neg$submitter_id] #filter for OPSCC tissues as in clinical data
opscc_tpm = opscc_tpm[c("MYB","RBL1"),] %>% t()
sp <- ggscatter(opscc_tpm, x = "MYB", y = "RBL1",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
) +
stat_cor(method = "pearson")+ # Add correlation coefficient
ggtitle("OPSCC TCGA samples")+
ylab("E2F1 log(TPM)")+
xlab("MYB log(TPM)")
sp## `geom_smooth()` using formula = 'y ~ x'
13 OPSCC sc signature
13.1 all samples
gene_status = set_clinical_data(clin_data = clinical_data,genes = opscc_signature,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "OPSCC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data[clinical_data$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
(p[[1]]+ggtitle("OPSCC samples"))/p[[2]]
13.2 HPV +
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV+")gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = opscc_signature,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "OPSCC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
(p[[1]]+ggtitle("OPSCC HPV+ samples"))/p[[2]]
13.3 HPV -
clinical_data_hpv_pos = clinical_data %>% filter(hpv_status == "HPV-")gene_status = set_clinical_data(clin_data = clinical_data_hpv_pos,genes = opscc_signature,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "OPSCC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data_hpv_pos[clinical_data_hpv_pos$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
(p[[1]]+ggtitle("OPSCC HPV- samples"))/p[[2]]
14 Additional mutations
gene_status = set_clinical_data(clin_data = clinical_data,genes = "TP53",tpm_data_frame = HNSC_tpm,stratify = params$stratify)
paged_table(gene_status)clinical_data_with_scores = clinical_data[clinical_data$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
main = "TCGA Set\n GBM",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
14.1 TP53
p = TCGAanalyze_survival(
data = clinical_data,
clusterCol = "tp53_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
p[[1]]/p[[2]]
14.2 Fisher
# plot
library(rstatix)
df = clinical_data[,c("tp53_status","hpv_status")]
test = fisher_test(table(df))
p = ggbarstats(
df,
tp53_status,
hpv_status,
results.subtitle = FALSE,
subtitle = paste0("Fisher's exact test", ", p-value = ",
test$p)
)
p
14.3 TP53 unmutated
clinical_data_for_test = clinical_data %>% filter(tp53_status == "tp53_unmutated")gene_status = set_clinical_data(clin_data = clinical_data_for_test,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data_for_test[clinical_data_for_test$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
(p[[1]]+ggtitle("OPSCC unmutated tp53 samples"))/p[[2]]
14.4 TP53 mutated
clinical_data_for_test = clinical_data %>% filter(tp53_status == "tp53_mutated") gene_status = set_clinical_data(clin_data = clinical_data_for_test,genes = hmsc_signatue,tpm_data_frame = HNSC_tpm,stratify = params$stratify,signature_name = "HMSC HPV \nsignature")
paged_table(gene_status)clinical_data_with_scores = clinical_data_for_test[clinical_data_for_test$submitter_id %in% rownames(gene_status),]
gene_status$patient = rownames(gene_status)
clinical_data_with_scores = left_join(x = clinical_data_with_scores,y = gene_status, by = c("submitter_id"="patient"))
p = TCGAanalyze_survival(
data = clinical_data_with_scores,
clusterCol = "gene_status",
height = 10, width=10,filename = NULL,
pval.method =T ,
xscale = "d_m",break.time.by = 365.2422,
xlab = "Time since diagnosis (months)"
)
(p[[1]]+ggtitle("OPSCC mutated tp53 samples"))/p[[2]]
15 session info
library(devtools)
session_info()## ─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.1 (2024-06-14)
## os Ubuntu 22.04.5 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2025-09-03
## pandoc 3.4 @ /usr/bin/ (via rmarkdown)
##
## ─ Packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## ! package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.1.0)
## annotate * 1.84.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## AnnotationDbi * 1.56.2 2021-11-09 [1] Bioconductor
## ape 5.8 2024-04-11 [1] RSPM (R 4.4.0)
## aplot 0.1.3 2022-04-01 [1] RSPM (R 4.1.0)
## babelgene 22.3 2022-03-30 [1] RSPM (R 4.1.0)
## backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0)
## bayestestR 0.15.0 2024-10-17 [1] RSPM (R 4.4.0)
## Biobase * 2.54.0 2021-10-26 [1] Bioconductor
## BiocFileCache 2.2.1 2022-01-23 [1] Bioconductor
## BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## BiocParallel 1.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## biomaRt * 2.50.3 2022-02-03 [1] Bioconductor
## Biostrings 2.62.0 2021-10-26 [1] Bioconductor
## bit 4.0.4 2020-08-04 [1] RSPM
## bit64 4.0.5 2020-08-30 [1] RSPM
## bitops 1.0-7 2021-04-24 [1] RSPM
## blob 1.2.3 2022-04-10 [1] RSPM
## boot 1.3-30 2024-02-26 [3] CRAN (R 4.4.1)
## broom 1.0.7 2024-09-26 [1] RSPM (R 4.4.0)
## bslib 0.9.0 2025-01-30 [1] RSPM (R 4.4.0)
## BWStest 0.2.3 2023-10-10 [1] RSPM (R 4.4.0)
## cachem 1.0.6 2021-08-19 [1] RSPM
## car 3.0-12 2021-11-06 [1] RSPM (R 4.1.0)
## carData 3.0-5 2022-01-06 [1] RSPM (R 4.4.0)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.4.0)
## chromote 0.3.1 2024-08-30 [1] RSPM (R 4.4.0)
## circlize * 0.4.16 2024-02-20 [1] RSPM (R 4.4.0)
## cli 3.6.3 2024-06-21 [1] RSPM (R 4.4.0)
## clue 0.3-66 2024-11-13 [1] RSPM (R 4.4.0)
## cluster 2.1.6 2023-12-01 [3] CRAN (R 4.4.1)
## clusterProfiler * 4.2.2 2022-01-13 [1] Bioconductor
## codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.1)
## colorspace 2.0-3 2022-02-21 [1] RSPM
## ComplexHeatmap * 2.10.0 2021-10-26 [1] Bioconductor
## conflicted * 1.2.0 2023-02-01 [1] RSPM (R 4.4.0)
## correlation 0.8.6 2024-10-26 [1] RSPM (R 4.4.0)
## cowplot 1.1.1 2020-12-30 [1] RSPM (R 4.1.0)
## crayon 1.5.1 2022-03-26 [1] RSPM
## curl 6.0.0 2024-11-05 [1] RSPM (R 4.4.0)
## data.table * 1.14.2 2021-09-27 [1] RSPM (R 4.1.0)
## datawizard 0.13.0 2024-10-05 [1] RSPM (R 4.4.0)
## DBI 1.2.3 2024-06-02 [1] RSPM (R 4.4.0)
## dbplyr 2.3.4 2023-09-26 [1] RSPM (R 4.4.0)
## DelayedArray 0.24.0 2022-11-01 [1] Bioconductor
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## future.apply 1.8.1 2021-08-10 [1] RSPM (R 4.1.0)
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## viridis * 0.6.2 2021-10-13 [1] RSPM
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## XML * 3.99-0.9 2022-02-24 [1] RSPM
## xml2 1.3.3 2021-11-30 [1] RSPM (R 4.1.0)
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##
## [1] /sci/labs/yotamd/lab_share/avishai.wizel/Projects/libs
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library
## [4] /sci/home/avishaiw/R/x86_64-pc-linux-gnu-library/4.4
##
## V ── Loaded and on-disk version mismatch.
## P ── Loaded and on-disk path mismatch.
##
## ─ Python configuration ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## python: /sci/labs/yotamd/lab_share/avishai.wizel/python_envs/Virtual_env/cnmf_dev/bin/python
## libpython: /sci/home/avishaiw/.local/share/r-miniconda/envs/r-reticulate/lib/libpython3.8.so
## pythonhome: /sci/labs/yotamd/lab_share/avishai.wizel/python_envs/Virtual_env/cnmf_dev:/sci/labs/yotamd/lab_share/avishai.wizel/python_envs/Virtual_env/cnmf_dev
## version: 3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:35) [GCC 10.4.0]
## numpy: /sci/labs/yotamd/lab_share/avishai.wizel/python_envs/Virtual_env/cnmf_dev/lib/python3.8/site-packages/numpy
## numpy_version: 1.24.4
##
## NOTE: Python version was forced by use_python() function
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────